Science, Faculty of
Zoology, Department of
DSpace
UBCV
Zareyan, Shahab
Master of Science - MSc
University of British Columbia
Trivers has hypothesized that self-deception in our species has evolved for the better deception of others: in an arms race between deception and deception-detection, the dishonest individuals evolve ever-more complex trickery and the deceived an ever-more refined ability to distinguish honesty from deception. Detection at some point becomes so precise that a degree of self-deception can evolve to avoid emitting secondary cues that otherwise give away the deceit. In an attempt to formalize this, we focus on aspects of self-deception that can be generalized to non-humans, as human self-deception by itself relies on concepts that are difficult to define or to apply to other organisms. We formally explore one central aspect of Trivers' hypothesis: the evolution of costly and well-integrated or deep deceptive morphs that span multiple signals and cues. We demonstrate that the depth of deception in a communicative interaction is correlated with the number of signals detected, the cost of errors in judgment for signal detectors, and the benefits of successful deception. We also show that the frequency of well-integrated deceptive strategies is highest when the cost of errors in judgment is high and the cost of detection of other less well-integrated forms of deception is low. These results may partially explain variation in deception in nature and provide researchers with predictions that can be tested empirically, with obvious implications for self-deception. Moreover, we argue that self-deception under Trivers' hypothesis is the product of a hierarchical system, in this case, the cognitive system, with some parts (ex. the subconscious) controlling and ultimately manipulating the information that is received by other parts (ex. the conscious). Although we do not model this, we emphasize that hierarchies are integral parts of many systems such as gene regulatory networks. Thus, in response to an arms race with an adversary, these hierarchies can potentially evolve ``internal deception'', with some parts transmitting manipulated information to other parts to prevent information leakage. We argue that modeling how properties of hierarchies affect the evolution of deception can allow for testable predictions and a better understanding of deception and self-deception in general.
A Sheep in Wolf’s Clothing: Levels of Deceit andDetection in the Evolution of CommunicationbyShahab ZareyanB.Sc., Honours in Biology and Minor in Mathematics, 2017a thesis submitted in partial fulfillmentof the requirements for the degree ofMaster of Scienceinthe faculty of graduate and postdoctoralstudies(Zoology)The University of British Columbia(Vancouver)December 2018c© Shahab Zareyan, 2018The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:A Sheep in Wolf’s Clothing: Levels of Deceit and Detectionin the Evolution of Communicationsubmitted by Shahab Zareyan in partial fulfillment of the requirementsfor the degree of Master of Science in Zoology.Examining Committee:Christoph Hauert, MathematicsCo-SupervisorSarah Otto, ZoologyCo-SupervisorDarren Irwin, ZoologyAdditional ExaminerAdditional Supervisory Committee Members:Michael Doebeli, Zoology and MathematicsSupervisory Committee MemberiiAbstractTrivers has hypothesized that self-deception in our species has evolved for thebetter deception of others: in an arms race between deception and deception-detection, the dishonest individuals evolve ever-more complex trickery andthe deceived an ever-more refined ability to distinguish honesty from de-ception. Detection at some point becomes so precise that a degree of self-deception can evolve to avoid emitting secondary cues that otherwise giveaway the deceit.In an attempt to formalize this, we focus on aspects of self-deceptionthat can be generalized to non-humans, as human self-deception by itselfrelies on concepts that are difficult to define or to apply to other organisms.We formally explore one central aspect of Trivers’ hypothesis: the evolutionof costly and well-integrated or deep deceptive morphs that span multiplesignals and cues. We demonstrate that the depth of deception in a commu-nicative interaction is correlated with the number of signals detected, thecost of errors in judgment for signal detectors, and the benefits of success-ful deception. We also show that the frequency of well-integrated deceptivestrategies is highest when the cost of errors in judgment is high and the costof detection of other less well-integrated forms of deception is low. Theseresults may partially explain variation in deception in nature and provideresearchers with predictions that can be tested empirically, with obviousimplications for self-deception.Moreover, we argue that self-deception under Trivers’ hypothesis is theproduct of a hierarchical system, in this case, the cognitive system, withsome parts (ex. the subconscious) controlling and ultimately manipulatingiiithe information that is received by other parts (ex. the conscious). Al-though we do not model this, we emphasize that hierarchies are integralparts of many systems such as gene regulatory networks. Thus, in responseto an arms race with an adversary, these hierarchies can potentially evolve“internal deception”, with some parts transmitting manipulated informa-tion to other parts to prevent information leakage. We argue that modelinghow properties of hierarchies affect the evolution of deception can allowfor testable predictions and a better understanding of deception and self-deception in general.ivLay SummaryIt has been hypothesized that self-deception has evolved for the better de-ception of others: those that believe in their lies are better at convincingothers. The focus of this thesis is using mathematical models to investigatewhether aspects of the hypothesis can be generalized to non-humans andthus can be empirically tested and placed on a more firm evolutionary foun-dation. Many deceptive strategies in nature, like self-deception, are costlyand exaggerated; they involve dishonest signaling as well as attempts to hidecues of dishonesty. We show that these well-integrated forms of deceptionevolve when many signals are detected, when there is a large benefit to thedeception, and when the cost of errors in judgment for those that are de-ceived is high. We further comment on other aspects of self-deception thatcan be tested in non-humans.vPrefaceChapter 2 has been submitted for publication. The candidate’s contributionsare outlined below:• Zareyan, S. Otto, S. P. and Hauert, C. (2019) A Sheep in Wolf’sClothing: Levels of Deceit and Detection in the Evolution of Commu-nication. submitted.Conceived and designed the experiments: SZ CH SPO. Performed theexperiments: SZ. Analyzed the data: SZ SPO CH. Wrote the paper:SZ CH SPO.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Supplementary Material . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . xii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.0.1 Thesis Overview . . . . . . . . . . . . . . . . . . . . . 11.0.2 Trivers’ Hypothesis . . . . . . . . . . . . . . . . . . . . 11.0.3 Evidence for Trivers’ Hypothesis . . . . . . . . . . . . 41.0.4 Difficulties Modelling Self-Deception . . . . . . . . . . 51.0.5 Deceptive Arms Races . . . . . . . . . . . . . . . . . . 72 Evolution of Well-Integrated Deception in A Simple Sig-naling Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Evolution of Deception . . . . . . . . . . . . . . . . . . . . . . 112.3 Evolution of Deception-Detection . . . . . . . . . . . . . . . . 16vii2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.5 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.0.1 Hierarchical Information Processing Systems . . . . . 273.0.2 Concluding Remarks . . . . . . . . . . . . . . . . . . . 33Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34viiiList of FiguresFigure 2.1 Surface dynamics around the (H, D; T, I) equilib-rium under the (a) standard and the (b) adjusted repli-cator dynamics. For both panels, β = 2, γ = 1, c = 2,and s = 1. Equilibria are indicated by block dots. . . . . 16Figure 2.2 Cycles of invasions and re-invasions upon the intro-duction of well-integrated deceivers. . . . . . . . . . . . . 21Figure 2.3 Dynamics of prey (blue) and predators (black)around the (H, D, W; T, D, I) equilibrium pointunder the (a) standard and (b) adjusted replicator dy-namics. The time period over which the replicator equa-tions were numerically solved is indicated at the bottom ofeach sub-figure. For both panels, t indicates time, β = 3,c = 3, s = 1, γ = 2, δ = 1, and g = 2. . . . . . . . . . . . . 21ixFigure 2.4 Response to perturbations away from the tri-morphicequilibrium in prey (blue) and predator (black)populations. For all panels, we perturb the system indirections indicated at the top of the figure. We thendetermine the response to each perturbation using thestandard replicator dynamics (second row), and visualizethe response by drawing vector fields that depict selectiongradients following each perturbation. Note that perturb-ing the system in one of the populations induces selectiononly in the other population - not in the original popula-tion. Parameters: β = 3, γ = 2, δ = 1, c = 3, s = 1, andg = 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Figure 3.1 Schematic representation of the hierarchical infor-mation processing system underlying Trivers’ hypoth-esis on the evolution of self-deception. S: subconsciousmodule; C: conscious module; 2o: secondary module. . . . 28xList of SupplementaryMaterialA Mathematica file is attached to this thesis. It contains all the calculationsfor deriving the results in Chapter 2, as well as the code for recreating allthe figures.xiAcknowledgmentsI would like to thank my supervisors, Christoph and Sally, for their uncondi-tional support; for accepting me as a student even though my undergraduatebackground was different than their research focus; and for letting me pur-sue the ambitious questions I was interested in. I am deeply grateful for this- I do not think this would have been possible at any other institution orresearch group.I would like to thank the members of the Otto, Hauert, and Doebeli labs,and Michael Doebeli in particular, my committee adviser, for valuable feed-back on my projects, and for their support throughout my degree. I am alsograteful to Darren Irwin, who, as the departmental examiner, thoroughlyassessed the scholarship of this thesis and provided very helpful suggestions.I would like to thank Ali for constant, forceful, and in depth critique of thework and ideas related to the work, the likes of which - with regards tothe depth of the critique - I have rarely seen. I also like to thank him formuch-needed guidance on how to rigorously apply the scientific method toquestions of interest, how to ask questions, and what type of questions toask, an influence which permeates every single sentence in this thesis.I would like to thank my family who, despite unfamiliarity with my research,gave me all the tools I needed to survive. I like to particularly thank Yasfor giving me much-needed hope.Finally, I am grateful to NSERC CGS-M and the Zoology Department forfinancial support.xiiChapter 1Introduction1.0.1 Thesis OverviewThe core of this thesis, presented in Chapter 2, focuses on modeling theco-evolutionary arms race between deception and deception-detection. Thetype of deception modeled is generic, and thus, qualitatively, the resultsof the model apply to many different systems of communication in nature.What is not addressed in the thesis core, however, is the original motivationbehind the project, the original question, and specifically how the modelingframework in Chapter 2 relates to that question. We address these here byplacing this work in the broader, but more vague, context of self-deception.We then connect this material to the formal model in Chapter 2. In Chapter3, we expand on the relevance of the results of Chapter 2 for the originalquestion.1.0.2 Trivers’ HypothesisThe original intention of the thesis was to expand a hypothesis put forwardby Robert Trivers on the arms race between deception and detection and theeffects of that on cognitive evolution in humans [38, 40]. In the 1970s, Trivershad applied the logic of natural selection to a variety of social behaviors andhad provided predictions that are to this day being tested in a variety oforganisms [39]. Yet he encountered an interesting puzzle around the same1time [39]:“On the one hand, our sense organs have evolved to give us a mar-velously detailed and accurate view of the outside world (...) But once thisinformation arrives in our brains, it is often distorted and biased to our con-scious minds. We deny the truth to ourselves. We project onto others traitsthat are in fact true of ourselves and then attack them! We repress painfulmemories, create completely false ones, rationalize immoral behavior, actrepeatedly to boost positive self-opinion, and show a suite of ego-defensemechanisms. Why?” (p. 2).Trivers used the phrase self-deception, or the construction and the subse-quent belief in a false sense of the world and the self [10], to encapsulate allthese phenomena. His interest in the question was warranted particularlygiven the implications of self-deception for our intentions: if one can fabri-cate a false sense of the world, one can also fabricate one’s intentions. Thisis a source of moral dilemmas as intention plays a central part in our concep-tion of morality [23]: we praise ourselves or our compatriots for “trying” todo the right thing or simply “having” benevolent thoughts; our criminal codeis heavily based on wrongdoers’ intentions (intentional battery, intentionalhomicide, intentional infliction of emotional distress, etc.); and we justifywars that kill hundreds of thousands of innocent civilians by the “good”intentions of our nation states. Given the dilemmas to which self-deceptiongives rise, it is of no surprise that great works of philosophy, religion, and lit-erature, since the dawn of history, have grappled with our tendency towardsexercising self-deception and its colossal consequences [10].Nevertheless, at the time when Trivers was puzzled by the concept,there existed very few testable hypotheses and little empirical work onself-deception. Indeed, the scientific study of self-deception has historicallylagged behind the many debates it has elicited, perhaps because the phe-nomenon has been questioned on all possible grounds, best illustrated bythe many attempts to try to define exactly what constitutes self-deception[10]. How can an organism be the deceiver and the deceived at the sametime? Self-deception perhaps necessitates the conception of a non-unitarymind - itself a contested view [4] - with some parts deceiving other parts.2To demonstrate that an individual is self-deceiving, one would need to de-termine what these units are, assess the information stored in each one ofthem, and assess how they share information with each other, a task that iscomplicated by the organism hiding the information from itself and others.These roadblocks are further exacerbated when one attempts to apply theconcept to non-humans.More phenomenological studies are, however, available on certain humanbiases that are arguably consistent with self-deception. Examples include:1) the better-than-average effect, or the belief of the majority that they arebetter than average in their attributes [12], as when 94% of academics believethat their teaching abilities are better than the average at their institution[5]; 2) self-handicapping, or engaging in a costly behavior that ensures failureon tasks that one is likely to fail anyways; the benefit being that the self-handicapper can then blame the costly behavior, as opposed to ineptitude,for his/her failures, and by doing so, maintaining self-confidence in a self-deceiving manner [41]; and 3) not gathering critical information about one’shealth status, or doing so in a biased way, out of fear of obtaining diagnosticresults that one might not want to hear [8, 11].In light of observations similar to above, the dominant school of thoughtat the time of Trivers considered self-deceptive biases as necessary illusionsrequired for us to feel better about ourselves and to maintain our mentalhealth [42]. Trivers, being partly a naturalist, observing psychological war-fare in primates such as baboons, began first by asking questions aboutself-deception’s survival value [40]. In that context, there was a problemwith the aforementioned explanation [40]:“Even if being happier is associated with higher survival and reproduc-tion, as expected, why should we use such a dubious - and potentially costly- mechanism as self-deception to regulate our happiness?” (p. 4).Various hypotheses can provide an answer to the above. Trivers pro-posed an alternative: self-deception is an offensive strategy in the serviceof deceit - those that believe in their own lies are also better at convinc-ing others of their lies [38]. This, Trivers postulated, can originate from anevolutionary arms race, where evolution of deception-detection selects for3better deception, which then selects for better detection. At some point,deception-detection becomes so accurate that an organism might benefitfrom self-deceiving in order to not give away secondary cues of its decep-tion.The advantage of Trivers’ hypothesis in comparison with previous at-tempts is that it, simultaneously, addresses three of Tinbergen’s four cate-gories of explanation of animal behavior [37]: 1) phylogeny: what can leadto the evolution of self-deception; 2) survival value: what are the benefitsof self-deception to an organism that exercises it; and 3) mechanism, whichis implicit in the hypothesis, and which we discuss in more detail in theconclusion of this thesis. As a consequence, it suggests avenues of researchthat previously could not be conceived.1.0.3 Evidence for Trivers’ HypothesisThe hypothesis has led to the discovery of some interesting patterns. Aseries of recent papers show that people, when incentivized to argue forsome position, are likely to bias their initial perception of that position[1, 28] or are likely to bias their information gathering so that they aremore likely to encounter information that supports their position [34]. As aconsequence, these people become more convincing in the position for whichthey are asked to argue, in line with Trivers’ hypothesis. Most importantly,the biases are maintained even when they are incentivized to objectivelyassess and correct their position, indicating that they perhaps truly believein them, that they are self-deceived, and that their self-deception comes atthe cost of inflexibility [34]. Although the cost in such experimental settingsis negligible, one can imagine that informational distortion of this type,when practiced regularly, can have substantial consequences for those thatdistort.On the theoretical side, three important papers have attempted to modelaspects of Trivers’ hypothesis [2, 18, 26]. All papers use some version of thehawk-dove game [33]: players are paired with one another and are given theoption of engaging in a fight with their opponent over a resource. If no one4fights, no one gets anything. If one fights but the other does not, the onethat chose to fight will get all the resource at no cost. If both fight, the out-come of the fight is determined by some attribute of each player. Players’decision to fight or not is determined by their perception of their attribute.Furthermore, players can signal their perception to their opponents, whichcan affect the opponent’s decision to engage in the fight. Self-deception isdefined as using an exaggerated version of the attribute to decide whetherto fight or not, and it can be in the service of deceit if the exaggeratedperception reduces the opponents’ chance of engaging in the fight. Onlyone of the studies defines a non-self-deceptive deceptive strategy, involvinga player that does not believe in an exaggerated attribute but neverthelesssignals an exaggerated trait value, and competes this strategy against theself-deceptive one [26]. These studies, in general, report that under appro-priate costs for self-deception as compared to the other strategies, and/orunder assumptions about the gullibility of the opponent, self-deception isfavored.1.0.4 Difficulties Modelling Self-DeceptionAlthough the above studies address the evolution of interesting strategies,none directly tackle why the self-deceptive strategies they considered areindeed self-deceptive as opposed to simple cognitive algorithms; that is,none fully addressed what aspects of their strategies are self-deceptive, andwhat aspects of self-deception are not included in their modelling framework.Most importantly, although the studies demonstrate how certain cognitivealgorithms outperform other algorithms, none attempted to test Trivers’hypothesis in its entirety, that is, none considered both the survival valueand the phylogeny aspects of Tinbergen’s criteria [37]. The original aimof the thesis was to begin with deception in a population and attempt todemonstrate how such forms, when placed in an arms race with deception-detection, evolve to become self-deceptive.Nevertheless, addressing the first of these problems, that is, attempt-ing to formulate a comprehensive mathematical definition of self-deception,5turned out to be unfruitful. Such definitions necessitate the formulation ofmany different complicated assumptions that inevitably limit the generalityof the model and hence constrain the applicability of its predictions. Forexample, what constitutes “self”? Does it refer to the conscious part ofthe brain? How are we to define consciousness mathematically? Unfortu-nately, these questions cannot be answered given our current understandingof humans and non-humans; attempting to provide an answer can lead tounproductive speculative work [9].Indeed, Trivers himself had, seemingly, recognized this problem and at-tempted to address it. In one of his books [39], Trivers emphasized thatalthough hypotheses on social behavior can be motivated by observations inour own species, for them to ultimately attain the vigor of a hard scienceas opposed to a just-so-story the researcher should focus on generalizing thebehavior to non-humans. In his most comprehensive work on self-deception[40], he states that: “it stands to reason that if our theory of self-deceptionrests on a theory of deception, advances in the latter will be especially valu-able”. Thus, by attempting to study analogous behaviors in non-humans,one is more likely to find more generalizable results with greater predictivepower that can then guide future research on the question of interest.Instead of focusing exclusively on self-deception, the main aim of thethesis thus shifted towards developing a better understanding of the evolu-tion of deception in arms races in general. Do deceivers evolve to concealsecondary cues of deception even when this would be of a very high cost?What would be the response of the deceived to sophisticated deception?Does the arms race continue forever, or does it settle on some stable equi-librium population state permanently? Yes, it is true that these questionsdo not address self-deception directly, but attempting to answer them canresult in robust, testable predictions that ultimately allow for a better un-derstanding of self-deception.61.0.5 Deceptive Arms RacesArguably, many antagonistic interactions in nature are consequences of anarms race between deception and detection. In host-parasite interactions,the parasite attempts to mimic the host through novel strategies, and thehost is constantly engaged in differentiating between new forms of deceit andnon-deceit. In the context of sexual selection, individuals attempt to deter-mine the quality of potential mates, and potential mates attempt to misleadthrough ever-exaggerated signals. In predator-prey interactions, predatorsbenefit from knowing whether a potential prey is worth pursuing/eating,while prey evolve to appear toxic or able to defend themselves [24].The importance of arms races in signaling interactions was popularizedby Dawkins and Krebs in two papers in the 1980s [7, 21]. They argued that aplayer in a signaling interaction assumes either of two roles: a “manipulator”that sends deceptive signals as its interests do not overlap with a “mind-reader”, who nevertheless attempts to read through the deception. In anarms race between the two, signals and sensory systems evolve to becomeever-more complex.At the time, however, some argued that the “cynical” view of commu-nication proposed by Dawkins and Krebs [32] is not logical [29] because itfailed to explain why the deceived do not simply evolve to ignore signals in-stead of evolving to detect deception. The latter would make signaling futile,causing the collapse of communication altogether [29]. This so-called “log-ical flaw” of Dawkins’ and Krebs’ argument lead to its dismissal by some.Alternatively, Zahavi’s handicap principle [44] proposed that full honestymight be achieved in signaling systems if receivers evolve to only pay atten-tion to signals that are very costly to fake. Theoretical work on the subjectsubsequently focused on the analysis of Zahavi’s alternative [29].Indeed, to our knowledge, only one model has focused on the possibilityof an arms race between deception and deception-detection [20]. Instead oflooking for stable population equilibria in a game theoretical model, whichhas been the standard theoretical approach to the handicap principle [29,32], the authors use simulations to show that populations of signaler and7receiver neural networks can be locked into never-ending arms races. Theydemonstrate that in such contexts, deception jumps from one type of displayto another. Whether and when deception evolves to become exaggerated,however, is not explicitly addressed in their model.A main goal of Chapter 2 is to present an analytical model that si-multaneously encapsulates Dawkins’ and Krebs’ arms race perspective andZahavi’s handicap principle. Building on recent work [16, 45], we addressthe “logical flaw” of the arms race perspective and demonstrate that it is notreally a flaw, even when the standard game theoretical approach is utilized.We demonstrate that signaling systems can easily evolve stable forms of de-ception and detection, even when the deceived are given the opportunityto simply ignore signals. This then provides exactly the forces required forthe subsequent evolution of complex forms of detection, or “mind-reading”.Importantly, our model further finds that exaggerated displays, as argued inDawkins and Krebs, can evolve and be stably maintained in communicationsystems, resulting in rich evolutionary dynamics.8Chapter 2Evolution of Well-IntegratedDeception in A SimpleSignaling Game2.1 IntroductionNatural selection has led to the evolution of complex systems of signallingand communication across the tree of life [29, 35]. Whenever the interestsof interacting partners differ, however, communication systems are prone tocheating. Zahavi’s handicap principle suggests that communication remainsimmune to deception as long as the production cost of unreliable signalingis high [44]. When this does not hold, however, cheating invades and poten-tially sets off antagonistic co-evolution between deception and detection ofthat deception: selection could, for example, favor prey that communicatestrength and high escape capability to predators even if they are truly unde-fended. In response, predators that are better able to discern weak prey arefavoured, which in turn selects for prey that better hide their susceptibility,resulting in an arms race [7, 21].Implicit in the notion that better discrimination evolves is the assump-tion that false signals are poorly coordinated with other cues that indicate9the status of an organism (e.g., defended or undefended). Hence, a moreintegrated level of deception is possible if these other cues evolve and be-come consistent with the false signals. This, however, is not always possible:physico-chemical and developmental factors, for example, constrain trait co-variance, making certain alterations, such as the ones required for attainingsignal consistency, very costly or impossible. This is particularly true in thecontext of an arms race: although there are no reasons to assume that con-straints are at play initially, as the arms race proceeds, the number of signalsand cues detected increases, and with it the likelihood that some co-vary ina limited number of ways. In such cases, successful deception may still bepossible through highly pleiotropic and thus costly changes to the nature ofdevelopment in the organism, as this can supply the variation needed forsignal consistency. In light of these factors, our focus here is to determinehow these well-integrated or deep forms of deception - defined more formallyas deception involving manipulation of multiple signals and secondary cuesthat otherwise give away the deceit - evolve and are maintained in naturalpopulations.In nature, cases of multi-signal deception, spanning multiple domains(ex. morphology, behavior) and manifested through large-scale broad-actingdevelopmental changes, provide the most convincing evidence for stablemaintenance of deep deceivers, as such deception is expected to be of par-ticularly high cost given the nature of the underlying changes. The clearestexamples of this are: 1) female mimicry in a variety of animal species, in-cluding insects, fish, birds, and mammals, initiated, in some cases, early onduring development [27]; 2) Batesian mimicry in certain butterfly species,regulated by major developmental transcription factors [22, 25, 36]; 3) Mor-phological and behavioral mimicry of ants by spiders to evade predators [30],which, importantly, is also associated with substantial cost such as reductionin the number of eggs laid per eggsac due to the narrowing of the bodiesin the mimics [6]; and 4) mimicry of sticks by stick insects (Phasmatodea),which necessitates very thin bodies and has resulted in loss of some of theinternal organs that normally come in pairs [40].A more relevant example for humans is self-deception, defined as decep-10tion of the conscious part of the mind by the subconscious, either throughbiasing the gathering of information or biasing the gathered information.Trivers [38] hypothesized that self-deception evolved for better deception ofothers: by virtue of believing in their own lies, self-deceivers do not give sec-ondary cues that otherwise give away the deceit. Besides involving multiplesignals and cues, and thus fulfilling the criteria of a well-integrated decep-tion, its underlying mechanism of early information manipulation in thesubconscious is interestingly analogous to the early developmental changesthat underlie deception in the above examples. Self-deception is expectedto come at a cost, however, in this case due to a biased perception of realityand suboptimal decision-making, which has been outlined in many differentcontexts in humans [40].The aim of this work is to formulate that which is common to all of theabove cases in mathematical terms. We extend recent work on the evolutionof partially honest communication [16, 17, 45] by deriving analytical con-ditions for the evolution of stable systems with multi-signal detection andwell-integrated but costly forms of deception. We extend a signaling gamethat has traditionally been used to provide support for costly signaling the-ory to demonstrate shifts in the levels of deception. For clarity, we discussa prey-predator signaling interaction, though various aspects can easily beapplied to other situations.2.2 Evolution of DeceptionThe story behind the model is simple: A predator’s hunting success is af-fected by the type of prey it pursues. A prey that is strong, fast, andwell-armed is unlikely to be caught; the predator wastes energy attemptingto pursue such a prey, and it can also sustain injuries if the pursuit results inconfrontation. However, pursuit of undefended prey, which are by definitionslow and physically incapable of fighting back, is more likely to result incapture. As a result, it is beneficial for predators to distinguish between thedifferent prey types and pursue only the undefended.Thus, prey fall into two categories of defended and undefended, a trait11which we assume is exclusively a function of environmental factors such asprenatal or early-life conditions. The defended corresponds to those thatreceived adequate care and nutrition and the undefended to the ones thatdid not. We further assume that at any point in time a constant proportionof prey are defended, and all other are undefended.Prior to the evolution of any signaling, the predator population is ex-pected to be composed of those that always pursue prey. We refer to theseas indiscriminate (I). Their payoff, ΠI, is the weighted benefit of pursuingthe undefended, β, added to the weighted costs of pursuing the defended, γ:ΠI = β − γ (2.1)Hence, if there exists phenotypic differences between the two prey types,predators could evolve to detect these and pursue only prey with the unde-fended phenotypes. Such a predator, which we refer to as trusting (T), asit trusts the signal conveyed by the phenotype of the prey, has a payoff:ΠT = β (2.2)which is always higher than ΠI (equation 2.1).On the other hand, we assume prey pay a weighted cost c when they arepursued by predators when they are undefended1. Thus a prey that faces amixed population of indiscriminate and trusting predators with frequenciesxi and xt, respectively, will always pay the cost of pursuit:PH = −xic− xtc = −c (2.3)We refer to this prey as the honest (H) prey as it honestly communicatesits phenotype.With payoffs defined for the three strategies, we can determine whethertrusting predators invade a population of indiscriminate predators. The1We could also consider the cost associated with being pursued when defended. How-ever, as we will see, the difference between the payoff of the various prey strategies thatwe consider is manifested only when undefended. Including this additional cost does notchange the qualitative results.12evolutionary dynamics is governed by the replicator equations:x˙k = xk(Πk(y)− Π¯)(2.4a)y˙k = yk(Pk(x)− P¯)(2.4b)where xk(yk)is the frequency of predator (prey) strategy K, the dot denotesthe time derivative, and x (y) is a vector that contains frequencies of all thepredator (prey) strategies. The payoff of predator (prey) strategy K, Πk(y)(Pk(x)), is a function of the composition of the prey (predator) population,y (x). Π¯(P¯)corresponds to the average payoff of strategies in the predator(prey) population.Plugging in payoffs in equations 2.1-2.3 into the replicator dynamics 2.4,we realize that there exists only one locally stable equilibrium, consistingof honest prey and trusting predators (H; T) (for details, see supplement).Thus, an initial population of indiscriminate predators is always invadedand replaced by trusting ones. Note that we use the short-hand notation(prey strategies; predator strategies)to represent the equilibria.Evolutionary biologists have been concerned with the stability of theaforementioned equilibrium: it corresponds to a system where a reliable cueis being expressed, which allows predators to determine the defendedness ofthe prey. Given that the interests of prey and predator do not fully overlap,a natural question is, how does the detected trait remain reliable given thatthe undefended can evolve to mimic the defended? Recent advances haveshed some light on this problem [16, 45]. We review the main results below.First, we refer to the prey strategy that, when undefended, mimics thedefended in the trait that is used by trusting predators to differentiate be-tween the two types, as the deceptive (D) prey, and we assume that itsdeception comes at a cost s. Thus, the payoff of this strategy against apopulation of indiscriminate and trusting predators is:PD = −xi(c+ s)− xts = −xic− s (2.5)Conversely, the payoff of the two predator strategies against a mixed popu-13lation of honesty and deception is:ΠI = β − γ (2.6a)ΠT = yhβ (2.6b)Given the above, we can formally address the question of when a trait isexpected to remain a reliable indicator. A system composed of honest preyand trusting predators (H; T) is immune to invasion by mutant deceptiveprey when the fitness of the resident honest prey is higher than that of thedeceptive mutant, that is, when:s > c (2.7)or when the cost of deception is high (for details, see supplement). Thisis consistent with Zahavi’s handicap principle and costly signaling theory[14, 44].When s < c, however, deception invades and the populations move to-wards an equilibrium composed of deceptive prey and trusting predators (D;T). The trusting predators then respond as if all prey were defended, that is,they never pursue any prey. Consequently, their payoff at this equilibriumis zero (see 2.6 for yh = 0).What happens next? Given the natural assumption that pursuing preyis better than not pursuing any prey (β > γ), the payoff of predators atthe (D; T) equilibrium is lower than indiscriminate pursuit (β − γ > 0).Although one can devise new gain-of-function strategies capable of invadingthis (D; T) equilibrium, predators may easily revert to being indiscriminateas this simply involves a loss-of-function change.Thus, indiscriminate predators return and the populations evolve to-wards a (D; I) equilibrium. This, however, makes deception futile: thepayoff of honesty against an indiscriminate predator (−c) is always higherthan the payoff of the deceptive against the same predators (−c − s). Asa result, the honest strategy is favored and the populations return to theoriginal (H; I) equilibrium. Hence, the cycle restarts. All in all, the general14conclusion is that depending on the costs and the benefits of deception, thepopulations either remain at the fully honest equilibrium or undergo cyclesof invasions and re-invasions.The above story, however, is not complete because it focuses on thespread of one strategy at a time in the prey or the predator population. Im-portantly, there exists an interior equilibrium with non-vanishing frequen-cies for all four strategies (H, D; T, I), with (y∗h, y∗d; x∗t , x∗i ) = (1 − γβ ,γβ ;sc , 1 − sc ), with the asterisks representing equilibrium frequencies (seesupplement). This hybrid equilibrium [16, 45] is locally stable when:s < c (2.8)or in other words, when deception has a net benefit. The equilibrium turnsout to be a center surrounded by closed orbits (Figure 2.1 (a)) [15]. Forthe adjusted replicator dynamics [31], all equilibria remain the same [43],but in contrast, the cycles contract and ultimately converge to the interiorequilibrium (Figure 2.1 (b)). The only difference in the adjusted replicatordynamics is that fitness is always relative to mean fitness:x˙k = xkΠK(y)− Π¯Π¯(2.9a)y˙k = ykPK(x)− P¯P¯(2.9b)Thus, if the mean fitness of a population is low, selection is stronger andchange happens more rapidly under the adjusted regime as compared to thestandard dynamics.Other processes, including mutation [17], can also lead to the asymp-totic stability of the hybrid equilibrium. Thus, under slight and reasonablechanges to the underlying assumptions, the equilibrium with all the strate-gies becomes stable.The evolutionary stability of this equilibrium highlights that commu-nication can evolve under arbitrarily small signaling costs [45], contraryto Zahavi’s conjecture (equation 2.7, [44]). The cost of signals determinespresence/absence of deception in the population; but, the important point15Trusting Indiscriminate Trusting IndiscriminateHonestDeceptive(a) (b)Figure 2.1: Surface dynamics around the (H, D; T, I) equilib-rium under the (a) standard and the (b) adjusted replicatordynamics. For both panels, β = 2, γ = 1, c = 2, and s = 1.Equilibria are indicated by block dots.is that such deception evolves and can be maintained alongside the deceivedwithout destabilizing signaling systems.2.3 Evolution of Deception-DetectionAt the hybrid equilibrium, the response of the indiscriminate predator againstthe defended prey is a false positive (it pursues when it shouldn’t), and theresponse of the trusting predator against the undefended mimic is a falsenegative (it does not pursue when it should). The dearth of information be-hind these miscalculations creates opportunities for the rise of new strategiesthat ultimately change the nature of communication.First note that new strategies could take two different paths. The indis-criminate might mutate to detect a more reliable trait and completely relyon that trait for distinguishing between the two types. If this invades, thepopulation at least temporarily moves towards a fully-honest equilibrium.However, new deceptive forms can then evolve that deceive through the new16trait, and the population ultimately ends up at a hybrid equilibrium similarto above. Thus, qualitatively, not much would be different.Alternatively, the trusting predator could mutate to detect a new traitto distinguish the mimic from the model, that is, to detect deception. Forexample, deception through the old trait might cause physiological, anatom-ical, or behavioral changes that act as signs that give away the deceit: if, forinstance, the length of legs was originally detected to differentiate betweendefended and undefended prey, then the undefended could evolve longer legsto deceive the predators. An unintended consequence of this, however, isthat the ratio of leg length relative to other traits changes, and this asymme-try can then be detected as a secondary cue that gives away the deceit. Mostimportantly, evolution of such sophisticated detection can then favor theevolution of deceivers that deceive by manipulating the signals and the sec-ondary cues, a type of deception that may necessitate broad-acting changesfor attaining near-perfect mimicry - what we refer to as well-integrated ordeep deception.In contrast to the trusting and the indiscriminate predator strategies, thedeception-detector mutant, which we refer to as the detector (D), reducesinstances of both negatives and positives. We assume, however, that superiordetection comes at a cost δ. Thus, the detector’s payoff against a mixedpopulation of honest and deceptive prey is:ΠD = yh(β − δ) + yd(β − δ) = β − δ (2.10)Comparing equations 2.10 and 2.6, we realize detectors have a higher payoffthan trusting predators if:δ < ydβ, (2.11)that is, if the cost of improved detection is less than the weighted benefitsof reducing false negatives. Moreover, detectors have a higher payoff thanindiscriminate predators if:δ < γ, (2.12)that is, if the cost of improved detection is less than the cost of false positives17γ. In our case this corresponds to the cost of pursuing defended prey, but inother cases it could refer to the cost of providing food to an offspring thatdoes not need it, or mating with a low-quality individual.Detectors invade the hybrid equilibrium (H, D; T, I) whenever condi-tion 2.12 is met. Thus, the (H, D; T, I) hybrid equilibrium represents anevolutionary endpoint only if improved detection is too costly. In order toanalyze the consequences of invasion, we first adjust the payoffs of the hon-est and the deceptive prey to account for the new detecting predators withfrequency xd:PH = −xic− xtc− xdc = −c (2.13a)PD = −xi(s+ c)− xts− xd(s+ c) = −(xi + xd)c− s (2.13b)If detectors invade, an equilibrium analysis shows that the only locally sta-ble equilibrium is one composed of honest and deceptive prey, and trustingand detector predators (H, D; T, D). Given that this equilibrium exhibitsasymptotic stability under the adjusted dynamics [15], it is as robust as theoriginal hybrid equilibrium. This implies that populations where false pos-itives are very costly can afford multi-signal detection even if this reducesfitness. Thus, in natural populations, there should exist a positive correla-tion between the cost of false positives γ and the cost (δ) and complexity ofdetection.At the new (H, D; T, D) equilibrium, (y∗h, y∗d; x∗t , x∗d) = (1 − δβ , δβ ;sc , 1 − sc ). The frequency of deceivers δβ is lower than their frequency atthe previous equilibrium ( γβ ) because δ < γ. In other words, the originalhybrid’s prey population evolves towards more honesty upon evolution ofdeception-detection.Most importantly, the evolution of detection allows directional selectionalong new trait dimensions, allowing selection to act on secondary cues. Theprey can now evolve to deceive detectors by manipulating these secondarycues. This deception, nevertheless, necessitates concealment of not only theundefended state of the prey but also the deception itself. In contrast tosimple deception, it requires the decoupling of not only the primary trait18from the state of the organism, but also cues of deception from deceptionitself. For this reason, more sophisticated, or well-integrated levels of de-ception, are more likely to require costly broad-acting changes compared toadaptations involving manipulation of a single trait.Evolution of Well-integrated DeceptionGiven a population at the hybrid equilibrium discussed above, (H, D; T, D),we next consider the appearance of well-integrated deception in prey (W).We denote the collective cost of well-integrated deception by g, which wenaturally assume is greater than simple deception (g > s). The payoff ofthis strategy is:PW = −xi(c+ g)− xtg − xdg = −xic− g (2.14)Comparing equations 2.13 and 2.14 shows that in a population of trustingand detective predators, well-integrated deception has a higher payoff thansimple deception if:g − s < xdc (2.15)or in other words, when the residual cost of well-integrated deception (g−s)is less than the cost of being pursed when undefended, weighted by thefrequency of detectors. Note that more costly forms of well-integrated de-ception can evolve when detectors are more common. Furthermore, well-integrated deception has a higher payoff than honest prey against trustingand detector predators if:g < c (2.16)that is, if the overall cost of well-integrated deception does not exceed thecosts of being pursued. Importantly, the condition for well-integrated de-ceivers to invade the hybrid equilibrium (H, D; T, D) is also g < c. Thismeans that evolution of well-integrated deception does not depend on whatdeceptive forms already exist in the population. Well-integrated morphsevolve as long as they deceive the undeceived and as long as their cost isless than c. In this model, c is the weighted cost of being pursued when un-19defended. More generally, c represents the benefits of deception, or the costnot paid if the deception is successful. Thus, in parent-offspring signaling, cwould correspond to the amount of food parents provide to needy offspring,or in courtship displays, c would be the benefits of securing a mate. All inall, this means that there should exist a positive relationship between thebenefits (c), and the cost (g) and the depth of deception in nature.In order to analyze the invasion of well-integrated deception with fre-quency yw, we first modify the payoffs of the three predator strategic types:ΠI = β − γ (2.17a)ΠT = yhβ (2.17b)ΠD = yh(β − δ) + yd(β − δ)− ywδ = (yh + yd)β − δ (2.17c)Analysis of the replicator equations shows that, following the invasion, well-integrated deceivers replace honest prey, and the populations move towardsa (D, W; T, D) equilibrium. Thus, the prey population is composed onlyof deceivers, and as a result, the trusting predators are always subjected todeception: they never pursue any prey and their payoff is zero.What will happen next? If only one strategy is introduced at a time,the system shifts from one hybrid equilibrium to another in an endless cycle(see Figure 2.2 and appendix). If, however, we allow all strategic types tooccur, a central hybrid equilibrium exists, composed of honesty, normal de-ception, and well-integrated deception in the prey population, and trusting,detector, and indiscriminate in the predator population (H, D, W; T, D, I),with (y∗h, y∗d, y∗w; x∗t , x∗d, x∗i ) = (1 − γβ , δβ , γ−δβ ; sc , g−sc , 1 − gc ). The tri-morphic equilibrium of the replicator dynamics is again a neutrally stablecenter (see supplement). Numerical analysis of the dynamics under the ad-justed replicator equation shows convergence towards the equilibrium point(Figure 2.3). Thus, if enough variation exists in the population, the cyclicaldynamics are instead replaced by convergence towards a single equilibriumpoint, where costly detection and well-integrated deception co-exist indefi-nitely; even though the cost and benefits of pursuing and being pursed have20H, D; T, I H, D; T, DD, W; T, DD, W; D, IH, W; D, IH, W; T, IPreyH: HonestD: DeceptiveW: Well-integrated DeceptivePredatorT: TrustingD: DetectiveI: IndiscriminateFigure 2.2: Cycles of invasions and re-invasions upon the intro-duction of well-integrated deceivers.(a) (b)t t050001400honesttrustingdeceiverdetectorwell-integrated deceptionindiscriminateFigure 2.3: Dynamics of prey (blue) and predators (black)around the (H, D, W; T, D, I) equilibrium point un-der the (a) standard and (b) adjusted replicator dynamics. Thetime period over which the replicator equations were numeri-cally solved is indicated at the bottom of each sub-figure. Forboth panels, t indicates time, β = 3, c = 3, s = 1, γ = 2, δ = 1,and g = 2.remained the same, the costs of signaling, and the costs of detecting signals,have increased. A few things about this equilibrium are noteworthy.First, to develop a better understanding of why co-existence is possible,we visualize the interactions between the various strategies by plotting the21vector fields that represent the selection dynamics (Figure 2.4). As can beseen, the antagonistic interactions between pairs of strategies prevents anyone strategy from driving another extinct.Second, an increase in the cost of false positive responses (predators pur-suing defended prey, γ), is expected to result in an increase in the frequencyof undetected deceivers. This is seen both at the above equilibrium of (H, D,W; T, D, I), where the frequency of well-integrated deceivers, γ−δβ , increasesas γ increases, and at the original hybrid equilibrium of (H, D; T, I), whereundetected deception has frequency γβ .Third, although the total frequency of deceivers at this (H, D, W; T, D, I)equilibrium is the same as their frequency at the original (H, D; T, I) hybridequilibrium ( γβ ), the relative proportion of the two deceptive strategies isdetermined by δ, the cost of detecting simple forms of deception. Whenthis is not costly, well-integrated deception becomes more frequent thansimple deception. Thus, the frequency of well-integrated and costly forms ofdeception is likely to be high in a population when two conditions are met:1) false positives are costly (γ high) and 2) there is a low cost for detectingsimple forms of deception in the population (δ low).Lastly, there exists a correlation between the frequency of detectors ofsimple deception, and the costs of various forms of deception: the higher therelative cost of well-integrated deception as compared to simple deception(g− s), the higher the frequency of detectors. Thus, when γ and g are high,but δ and s are low, we expect the populations at the central hybrid equi-librium to consist mostly of predators that have costly fine-tuned detectionabilities, and prey that exercise elaborate and costly deception.2.4 DiscussionThis study emphasizes that, even with a low signaling cost, the spread ofdeception does not necessarily result in the collapse of communication. Wefind that deception can co-exist indefinitely in a heterogeneous populationalongside honesty, in line with [45]. The mechanism that allows for thisheterogeneity is a simple one: once common, deception is counter-selected22responsevisualizationperturbationhonest↑trusting↑deceiver↑detector↑well-integrateddeceiver↑indiscriminate↑trusting↑deceiver↑detector↑well-integrateddeceiver↑indiscriminate↑honest↑honesttrustingdeceiverdetectorwell-integrateddeceptionindiscriminateFigure2.4:Responsetoperturbationsawayfromthetri-morphicequilibriuminprey(blue)andpredator(black)populations.Forallpanels,weperturbthesystemindirectionsindicatedatthetopofthefigure.Wethendeterminetheresponsetoeachperturbationusingthestandardreplicatordynamics(secondrow),andvisualizetheresponsebydrawingvectorfieldsthatdepictselectiongradientsfollowingeachperturbation.Notethatperturbingthesysteminoneofthepopulationsinducesselectiononlyintheotherpopulation-notintheoriginalpopulation.Parameters:β=3,γ=2,δ=1,c=3,s=1,andg=2.23by the spread of strategies that ignore signals, which leads to the stableco-existence of multiple strategies in both populations. Given the simplicityof the mechanism, we expect it to apply to many systems of communicationin nature and expect them to be heterogeneous [45].Although previous work had predicted that heterogeneous signaling sys-tems are likely to include types that never respond to signals [45], herewe demonstrated that other alternatives are possible: indefinite existenceof deception in a population can result in selection for those mutants thatdetect and avoid the deception. Thus, besides the presence of constitutiveresponders, it is also likely for signal receivers to evolve the ability to detectmultiple signals or seek multiple sources of information. Deception is nev-ertheless maintained in the population because detection is costly and onlyfavored when deceivers are common, and deceivers are common only whendetectors are rare: neither can drive the other extinct.Importantly, we showed that, as a consequence of the evolution of multi-signal detectors, deceptive signaling can evolve to be well-integrated, pos-sibly involving broad-acting changes that allow for the decoupling of cuesof deception from the deception itself, even though costly. Certainly, themany forms of deception in nature that are manifested through major de-velopmental changes are consistent with this conclusion. Indeed, it is con-ceivable for the cost of deception due to broad-acting changes (g) to be sohigh that its net potential benefit (c − g) approaches zero, and yet suchcomplex and well-integrated deception is maintained stably in the popula-tion. Thus, high signaling cost should not be taken as necessarily providingsupport for the costly signaling theory, as complex forms of deception caninvade systems with simpler forms of deception, raising the apparent costsof signaling. Most importantly, the analysis predicts that in heterogeneoussignaling populations positive correlations exist between the degree to whichdeceptive morphs are well-integrated and: 1) the cost of false positives (γ);2) the value of the resources gained if the deception is successful (c) and 3)the number of signals detected, which is a measure of the amount of infor-mation the predator has about the prey, or, in other words, a measure ofpredator’s ability to differentiate between deceptive morphs and those that24are truly defended. Thus, the arms race between deception and detectiondoes not continue forever as it is regulated by the latter two parameters (cand γ), which are extrinsic factors not manipulated by the strategies.Note that our arguments are also in line with Trivers’ hypothesis onthe evolution of self-deception. Trivers notes: “It stands to reason that ifour theory of self-deception rests on a theory of deception, advances in thelatter will be especially valuable” [40]. Here we provide a systematic analysisof the dynamics of well-integrated deception to better understand levels ofdeception in nature and in humans.2.5 AppendixEach step of the cycle in Figure 2.2 is described below. All results areobtained through invasion and stability analysis of the replicator equations:• (D, W; T, D) → (D, W; D, I): All prey are deceiving (D or W),and hence trusting predators perform poorly such that indiscriminatepredators can invade and displace them as β − γ > 0.• (D, W; D, I)→ (H, W; D, I): The evolution of indiscriminate predatorsthen makes normal deception futile: their signals are either ignored bythe indiscriminate or their deception is detected by detectors. Thepayoff of honest prey (H) is always higher than that of deceivers (D,see Equation 2.13), and hence honest prey can invade and replacedeceivers.• (H, W; D, I) → (H, W; T, I): Against honest prey detection is futile:predators either face well-integrated deception, which they cannot de-tect, or honesty, against which deception-detection is unnecessary. Asa consequence trusting predators outperform and replace detectors (seeequation 2.17) because they avoid the costs of detection.• (H, W; T, I)→ (H, D; T, I): The absence of detecting predators renderscostly, well-integrated deception (W) useless. Thus, regular deception(D) is favored (see equations 2.13 and 2.14).25• (H, D; T, I) → (H, D; T, D): The presence of deceiving prey (D) nowgives detecting predators (D) an advantage over indiscriminate ones(I) and hence they invade and displace them.• (H, D; T, D) → (D, W; T, D): In order to avoid detection, well-integrated deception in prey (W) is now favoured, which replaces reg-ular deception (D). This recovers the first hybrid equilibrium.These invasions involve both reversions back to the ancestral strategies,as well as the evolution of detection and well-integrated deception, whichafter the completion of each round of cyclical invasions, should evolve anew.The latter are analogous to gain-of-function mutants, and it is thus naturalto assume that their evolution happens on a slower time-scale than rever-sions to the ancestors. For this reason, it can be argued that detection andwell-integrated deception are, in a sense, ephemeral phenomena: it takes along time for them to evolve, and once they evolve, they are not sustainedfor long, quickly replaced by their ancestral forms. As discussed in themain text, however, a stable equilibrium is possible if all six strategies aresimultaneously present.26Chapter 3ConclusionHow are we to extend the work presented above to develop a better under-standing of deception and self-deception in nature? As was mentioned inChapter 1, the difficulties we encountered in addressing self-deception natu-rally led to the reformulation of the original hypothesis and a shift of focustowards investigating one aspect of Trivers’ formulation, namely, the evolu-tion of well-integrated deception in an arms race. Can a similar approachbe used to explore other aspects of Trivers’ hypothesis? In other words,are there other properties of self-deception that can be explored withoutnecessarily exploring self-deception in its entirety?Below we demonstrate a second aspect of Trivers’ hypothesis that can beextended to non-humans and, if explored, can lead to valuable predictionsthat can be tested. The goal is not to present a comprehensive overview ofthis alternative interpretation of the hypothesis, but rather to summarizesome interesting applications of it and ultimately ague that the approach wehave pursued in this thesis, namely, addressing a complex human behavioralphenotype by analyzing its analogues in other contexts, is one of the mainways through which we can build a constructive science of self-deception.3.0.1 Hierarchical Information Processing SystemsTrivers, in his hypothesis, implicitly assumes a very particular cognitive ar-chitecture. Specifically, he assumes that a system capable of self-deception27top/deepmodulebottom/signalingmodulesinput SC2oadversaryorganismFigure 3.1: Schematic representation of the hierarchical infor-mation processing system underlying Trivers’ hypothesis onthe evolution of self-deception. S: subconscious module; C: con-scious module; 2o: secondary module.has to be: 1) a hierarchically organized network of modules within which2) information flows unidirectionally. Thus, under the most simple assump-tions, the brain is divided into three modules, with the subconscious modulesitting at the top of the hierarchy, receiving information from the environ-ment and transmitting it to: 1) the conscious module, within which dishon-est information can be constructed and communicated with the outside; and2) another module, which we call the secondary module, that regulates theproduction of secondary cues (Figure 3.1). We collectively refer to these twomodules as the signaling modules, as they send signals to the environment.Trivers specifically assumes that if the conscious module produces dishonestinformation, then the secondary module inevitably produces secondary cuesthat give away the deceit.Such a network structure can easily be formulated mathematically, withthe strategy of a module being defined as some simple input-output func-tion, and the strategy of the individual as the set of all the strategies of itsmodules. Deception can be defined as deviation from some honest strategyand implemented through changes in the functioning of the modules. Theevolution of networks can be driven by a population of adversaries attempt-28ing to decrypt the input of each network to the network’s disadvantage andthe adversary’s benefit. If it is impossible to coordinate fully all the signalingmodules at the time when executing the deception, then such a model wouldresemble exactly the model presented in Chapter 2, with well-integrated de-ception corresponding to a strategy that manipulates multiple signals andcues in a coordinated manner, through changes in the subconscious (deep)module to avoid producing secondary cues of deception.The most important consequence of the above reformulation of Trivers’hypothesis is that it highlights that what leads to deception jumping fromsignaling modules to deeper levels of the hierarchy are the aforementionedtwo assumptions of hierarchical organization and uni-directional informationflow, coupled with some constraints that prevent changes at the signalingmodules. This thus implies that any group of entities with these propertiescan evolve internal deception, that is, deception of some entities within thehierarchical system by other entities, if it is caught in an arms race with anexternal adversary that is capable of becoming superb at decoding the inputsof the hierarchy based on its outputs. Critically, there are many systemsthat share these properties: cognitive modules outside of the conscious andthe subconscious, a hierarchically-organized internal biological system, genesorganized in a regulatory network, or even individuals placed hierarchicallyin a group. This is critical as it demonstrates that analyzing evolution ofhierarchical networks not only applies directly to self-deception, but also toother contexts within which the predictions of these analyses can be tested.For the purposes of illustration, we discuss two of these applications below.Group Self-DeceptionAlthough not a biological system strictly, we first explore groups of individu-als organized in a hierarchy. Like a cognitive system involved in informationinterpretation and ultimately information transmission to the outside, infor-mation can flow from individuals placed at the top of a hierarchy to thoseat the bottom. Hence, it can be manipulated along the way. The informa-tion can then be transmitted to adversaries, such as other groups, whose29interests do not fully align with the original group’s interests. If the hierar-chy expands extensively with too many individuals at the base, leakage ofimportant information to the adversary cannot be prevented because fullycoordinating individuals at the bottom would be challenging. But leakagecan be prevented by having individuals at the top of the hierarchy deceivethose at the bottom, the indirect consequence of which would be the decep-tion of the adversary.Many entities fit the description above. The groups can represent cor-porations attempting to deceive consumers, a government attempting todeceive its people, or a highly unequal society with concentration of powerat the top attempting to prevent leakage of information to other nations.The colossal consequences of this self-deception can be further exacerbatedby those at the top becoming themselves self-deceived in order deceive theirin-groups better, resulting in nested self-deception. Indeed, Trivers, in hismost comprehensive work on self-deception [40], dedicates five chapters tothe so-called group self-deception:“When an organization practices deception towards the larger society,this may induce self-deception within the organization, just as deceptionbetween individuals induces individual self-deception.”He analyzes many different examples of this, from aviation and spacedisasters, to false historical narratives, to wars [40]. One interesteing case isthe explosion of NASA’s Challenger disaster about a minute after its launchin 1986, resulting in the death of all the crew members. Trivers, basing hisarguments on the thorough analysis of the disaster by Richard Feynman [13],provides many pieces of evidence that show deliberate attempt by NASA’sexecutives to suppress safety concerns that clearly demonstrated the highlikelihood of disaster. This led to the spread of misinformation within theorganization and ultimately the launch of this very risky flight. The reasonfor this internal deception? Trivers argues that NASA was originally foundedto land the first person on the moon before the Russians [40]. Once that wascompleted, what was left was a $5 billion bureaucracy that had to justify itsexistence to congress and the people. Glamour and cost took precedence oversafety in different ways, and independent internal investigation units were30suppressed in an apparent attempt to increase the appeal of NASA’s projectsto others. The effect was an unfortunate disaster, which, regrettably, wasto be repeated in a similar manner during the Columbia disaster seventeenyears later.Indeed, one can imagine that organizations and their employees that arebest able to hide internal weaknesses from the public and stockholders wouldbe more likely to persist, facilitating the evolution of organizational self-deception, until disaster strikes. This particularly applies to corporations,where internal organizational structure arguably affects the success of acorporation in a free competitive market. Clearly, this would not be a caseof biological evolution, but the forces that regulate markets are very similarto forces that control evolution in nature.Gene NetworksAnother context within which the hierarchical interpretation of Trivers’ hy-pothesis would be particularly useful are gene regulatory networks. Theseare organized hierarchically in that the activities of a few regulatory genesaffect the expression of the many genes downstream that directly code fortraits. Traits can then act as signals that other organisms can detect. Giventhat regulatory networks are integral parts of all organisms, changes to theirfunctioning are particularly essential for organisms caught in a communica-tion arms race attempting to escape detection by an adversary. Indeed, inthe introduction to Chapter 2, we provided a few examples of well-integrateddeception in nature that are manifested at the genetic level. Deception inthese cases can come about through mutations in the many genes that sitat the bottom of the hierarchy or the few changes in genes that sit at thetop. What tips the balance?There has been a long debate on this topic. The most clear exampleis the literature on the genetics of Batesian and Mullerian mimicry, wherethere has been debates as to whether complex mimetic traits are encodedby supergenes, that is, clusters of tightly linked genes each coding for a dif-ferent aspect of the mimicry, or by few changes in regulatory genes [3]. For31example, in the butterfly Heliconius numata, mimicry is regulated by a clus-ter of at least eighteen linked genes [19]. On the other hand, in butterfliesPapilio polytes [22, 25] and Papilio Dardanus [36], regions of chromosomesthat were originally thought to be supergenes, were found to be occupiedby single major regulatory genes. Many factors, such as the rate at whichthe adversary evolves, the rate at which new mutants are introduced intothe population, and the rate of recombination, can tip the balance in fa-vor of one mechanism or the other. Importantly, however, the structureof the genetic network itself can have major consequences on the form ofadaptation that evolves. The effects of this can be determined step-by-stepthrough simple models: tri-modular gene regulatory networks with simpleinput-output functions can be built, placed in an arms race, and subjectedto some baseline mutation and/or recombination rate. Through simulations,one can then analyze the relationship between the strengths of the variousconstraints and the evolution of hierarchically-deep versus shallow but ex-tensive deception at the signaling level.Other SystemsBesides the above cases, hierarchy is arguably a property of many internalbiological systems. These system can thus conceivably fall into an armsrace and evolve internal deception. However, specific empirical data onthese fronts is much-needed and is currently lacking.Overall, the above demonstrates that the hierarchical interpretation ofTrivers’ hypothesis applies widely, that it has substantial effects on the evo-lution of deception, and that, consequently, exploring it can result in a bet-ter understanding of variation in dishonest signaling in nature. The manyproperties of networks, such as the number of modules in each level of thehierarchy, the number of levels in the hierarchy, and how the modules areconnected to one another, can be modified and their impacts on the depthof deception can be analyzed using simple mathematical models. Given thatmuch is known about genetic networks, and that models of these networkscan lead to results that can potentially be tested, we believe that explor-32ing the hierarchical aspect of Trivers’ hypothesis in that realm would beparticularly fruitful.3.0.2 Concluding RemarksExploring the evolution of well-integrated deception in Chapter 2 lead toconcrete predictions: exaggerated deceptive displays evolve when many sig-nals are detected, and when the benefits of deception and the cost of falsepositives are high. More importantly, it brought to the forefront the hierar-chical property embedded within Trivers’ hypothesis. 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A sheep in wolf's clothing : levels of deceit and detection in the evolution of communication
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